AI in the Plastics Industry
How AI is Transforming the Plastics Industry: Opportunities, Challenges, and Practical Applications
An Interview with Kunststoff-Magazin
LabV recently introduced an AI-powered Material Intelligence Platform (MIP). It provides companies with direct access to their relevant material data and enables the automation of complex data analyses.
In this interview, Charles Jouanique, Chief Revenue Officer at LabV, discusses the practical applications of artificial intelligence in the polymer processing industry in particular – from its potential, and the challenges associated with its implementation.
Kunststoff-Magazin: Mr. Jouanique, artificial intelligence (AI) is increasingly being discussed in the plastics industry. Why do you see this topic as particularly important?
Charles Jouanique: The plastics industry faces enormous challenges. On the one hand, there is a need to develop innovative, high-performance materials that are also more sustainable and resource-efficient. On the other hand, quality assurance and compliance requirements are becoming more stringent, as demonstrated by DIN SPEC 91446 for recyclates. AI offers the ability to manage this complexity by efficiently analyzing data, identifying patterns, and providing actionable recommendations. This is a key advantage in an industry that relies heavily on data-driven decision-making.
Kunststoff-Magazin: Where do you see the greatest potential for AI in the plastics industry?
Charles Jouanique: The potential is vast, but three areas stand out: First, AI can support the development of new materials by optimizing material properties and formulations. Second, in quality control, where large volumes of data can be analyzed more quickly to detect deviations at an early stage. And third, in production process optimization—through predictive models that help minimize material waste and reduce energy consumption.
Kunststoff-Magazin: You mentioned quality control. Can you give a concrete example of how AI is used in this field?
Charles Jouanique: A great example is the quality control of plastic recyclates, i.e., recycled materials. Here, DIN SPEC 91446 plays a crucial role. This standard defines the data that companies must provide to ensure the quality of recyclates. It includes different levels of data quality, ranging from basic information like density and viscosity to more complex parameters such as heat resistance or particle size distribution.
For companies that need to comply with these standards, this means collecting, analyzing, and documenting a large amount of data. Doing this manually is extremely time-consuming and prone to errors. AI can significantly streamline this process by analyzing data quickly and reliably, identifying deviations, and clearly visualizing results. This not only ensures compliance with specifications but also helps maintain the long-term quality of recyclates.
Kunststoff-Magazin: What challenges do you see in implementing AI in the industry?
Charles Jouanique: One of the biggest challenges is data quality. AI requires structured, high-quality data to function effectively. However, many companies still have fragmented datasets spread across different systems. Another hurdle is acceptance. There is often resistance to new technologies, whether due to concerns about implementation complexity or the need to adapt existing processes. The key is to introduce simple, intuitive solutions that make adoption easier.
Charles Jouanique
Chief Revenue Officer at LabV
“One of the biggest challenges is data quality. AI requires structured, high-quality data to function effectively. However, many companies still have fragmented datasets spread across different systems.”
Kunststoff-Magazin: How is AI changing day-to-day work in the plastics industry?
Charles Jouanique: AI primarily automates routine tasks, freeing up time for creative and strategic work. A lab engineer, for example, no longer has to spend hours combing through Excel spreadsheets to identify deviations. Instead, AI delivers relevant analyses and visualizations at the push of a button. This not only makes processes more efficient but also leads to better decision-making. In the long run, AI will shift the way work is done in the plastics industry toward a more data-driven approach.
Kunststoff-Magazin: Sustainability is a major topic in the plastics industry. What role does AI play in this? AI changing day-to-day work in the plastics industry?
Charles Jouanique: A very significant one. AI helps to use materials more efficiently, optimize production processes, and reduce waste. For instance, AI can support the development of new polymer blends that consume fewer resources or enable recyclable alternatives. It also plays a crucial role in the quality control of recyclates, ensuring compliance with stringent standards like DIN SPEC 91446. By enabling data-driven decisions, AI is helping to drive sustainability in the industry.
Kunststoff-Magazin: What developments do you expect in AI for the plastics industry in the coming years?
Charles Jouanique: I believe we will see a deeper integration of AI across the entire value chain. From designing new materials to production and recycling, AI will become increasingly embedded. Particularly exciting are developments in predictive analytics, which will allow for highly precise forecasting of material properties at early development stages. AI could even help create entirely new materials tailored to future requirements.
Kunststoff-Magazin: When will AI become relevant in the plastics industry?
Charles Jouanique: AI is not a distant future technology—it is already a valuable tool in the plastics industry today. Companies that adopt this technology early can not only enhance their efficiency but also strengthen their innovation capabilities and sustainability efforts. It’s worth embracing these changes and seeing AI as an opportunity to drive lasting transformation in the industry.
Kunststoff-Magazin: Mr. Jouanique, thank you for the conversation!
Charles Jouanique: Thank you!